Merge pull request #457 from spsanderson/development

prep for CRAN

R/utils-aic-beta.R

@@ -17,11 +17,13 @@
 #' @details
 #' Initial parameter estimates: The choice of initial values can impact the
 #' convergence of the optimization.
+#'
 #' Optimization method: You might explore different optimization methods within
 #' optim for potentially better performance.
 #' Data transformation: Depending on your data, you may need to apply
-#' transformations (e.g., scaling to [0,1] interval) before fitting the beta
+#' transformations (e.g., scaling to `[0,1]` interval) before fitting the beta
 #' distribution.
+#'
 #' Goodness-of-fit: While AIC is a useful metric for model comparison, it's
 #' recommended to also assess the goodness-of-fit of the chosen model using
 #' visualization and other statistical tests.
@@ -65,7 +67,7 @@ util_beta_aic <- function(.x) {
 
   # Fit beta distribution using optim
   fit_beta <- stats::optim(
-    c(trunc(pe$shape1), trunc(pe$shape2), 0),
+    c(round(pe$shape1, 0), round(pe$shape2, 0), 0),
     neg_log_lik_beta,
     data = x
   )

R/utils-aic-chisq.R

@@ -13,8 +13,9 @@
 #'
 #' @examples
 #' # Example 1: Calculate AIC for a sample dataset
-#' data <- c(1, 2, 3, 4, 5)
-#' util_chisq_aic(data)
+#' set.seed(123)
+#' x <- rchisq(30, df = 3)
+#' util_chisq_aic(x)
 #'
 #' @return
 #' The AIC value calculated based on the fitted chi-square distribution to the provided data.
@@ -40,7 +41,7 @@ util_chisq_aic <- function(.x) {
 
   # Fit chi-square distribution to sample data (rchisq)
   fit_chisq <- stats::optim(
-    c(pe$degrees_of_freedom, pe$ncp),
+    c(pe$dof, pe$ncp),
     neg_log_lik_chisq,
     data = x
   )

R/utils-aic-logistic.R

@@ -34,7 +34,7 @@
 #' # Example 1: Calculate AIC for a sample dataset
 #' set.seed(123)
 #' x <- rlogis(30)
-#' util_logistic_aic(data)
+#' util_logistic_aic(x)
 #'
 #' @return
 #' The AIC value calculated based on the fitted logistic distribution to the provided data.

R/utils-check-dupe-rows.R

@@ -7,7 +7,7 @@
 #' @description
 #' This function checks for duplicate rows in a data frame.
 #'
-#' @param data A data frame.
+#' @param .data A data frame.
 #' @return A logical vector indicating whether each row is a duplicate or not.
 #' @details This function checks for duplicate rows by comparing each row in the
 #' data frame to every other row. If a row is identical to another row, it is